This article presents an original method for automatic generation of sign language (SL) content by means of the animation of an avatar, with the aim of creating animations that respect as much as possible linguistic constraints while keeping bio-realistic properties.
These are: news headlines in French, translations of these headlines into LSF in the form of videos showing animations of a virtual signer, gloss annotations of the “traditional” type—although including additional information on the context in which each gestural unit is performed as well as their potential for adaptation to another context—and AZee representations of the videos, i. e. formal expressions capturing the necessary and sufficient linguistic information.
no code implementations • • John C. McDonald, Rosalee Wolfe, Eleni Efthimiou, Evita Fontinea, Frankie Picron, Davy Van Landuyt, Tina Sioen, Annelies Braffort, Michael Filhol, Sarah Ebling, Thomas Hanke, Verena Krausneker
Development of automatic translation between signed and spoken languages has lagged behind the development of automatic translation between spoken languages, but it is a common misperception that extending machine translation techniques to include signed languages should be a straightforward process.
This article presents an original method for Text-to-Sign Translation.
While the research in automatic Sign Language Processing (SLP) is growing, it has been almost exclusively focused on recognizing lexical signs, whether isolated or within continuous SL production.
This paper presents MEDIAPI-SKEL, a 2D-skeleton database of French Sign Language videos aligned with French subtitles.
In a lot of recent research, attention has been drawn to recognizing sequences of lexical signs in continuous Sign Language corpora, often artificial.
1 code implementation • 22 Aug 2019 • Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudrealt, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, Meredith Ringel Morris
Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture.
In this paper, we describe DEGELS1, a comparable corpus of French Sign Language and co-speech gestures that has been created to serve as a testbed corpus for the DEGELS workshops.